Hidden barriers to AI adoption in hospitality SMEs

A new study has found that corporate social responsibility (CSR) practices in small restaurant businesses do not automatically drive artificial intelligence adoption, instead shaping how managers perceive and evaluate the technology. The findings challenge common assumptions that sustainability-oriented firms are more likely to embrace digital transformation, revealing a more complex, value-driven decision process inside micro and small enterprises.

Published in Sustainability, the study titled "Corporate Social Responsibility Practices, Managerial Attitudes Toward Artificial Intelligence, and AI Adoption in Micro and Small Restaurant SMEs" examines how CSR, managerial attitudes, and financial performance interact in determining AI uptake across 157 Slovenian restaurant SMEs.

CSR does not uniformly encourage AI adoption

The study finds that CSR practices influence AI adoption indirectly, primarily by shaping managerial attitudes rather than directly driving implementation. However, this influence varies significantly depending on the type of CSR activity.

Employee-focused CSR practices, such as fair wages, safe working conditions, and staff well-being, showed no meaningful impact on managers' attitudes toward AI. Researchers attribute this to the fact that such practices are already deeply embedded in restaurant operations, functioning as baseline expectations rather than differentiating factors in decision-making. In small hospitality firms, these practices are often institutionalized through regulation and industry norms, leaving little variation across businesses.

On the other hand, environmental CSR practices, including waste reduction, energy efficiency, and sustainable sourcing, were found to have a negative association with attitudes toward AI. Managers who prioritize environmental responsibility tend to evaluate AI more cautiously, reflecting concerns about the technology's relevance to sustainability goals.

This cautious stance is not rooted in resistance to innovation but in perceived misalignment. In many small restaurants, AI is primarily associated with operational tools such as booking systems, chatbots, and customer management platforms rather than direct environmental improvements. As a result, managers focused on sustainability may question whether AI meaningfully contributes to their environmental objectives.

The study also points to broader perceptions of AI's environmental footprint. Growing awareness of the energy demands of data centers and computational systems may lead environmentally conscious managers to view AI as resource-intensive rather than eco-friendly. In businesses where sustainability is expressed through tangible practices like reducing food waste or sourcing locally, AI's benefits may appear abstract or indirect.

Additionally, the identity of many small restaurants plays a role. Businesses that emphasize authenticity, craftsmanship, and human interaction may perceive AI as undermining these values. This is particularly relevant in establishments that position themselves as environmentally responsible or community-oriented, where technological automation may conflict with their brand identity.

Overall, the findings suggest that CSR acts as a lens through which managers assess AI, making them more selective rather than more receptive. This results in an asymmetric relationship where some CSR dimensions encourage caution rather than adoption.

Managerial attitudes emerge as the decisive factor

While CSR shapes perceptions, the study identifies managerial attitudes toward AI as the most critical driver of adoption. Positive attitudes, including trust in AI, perceived usefulness, and openness to its applications, are strongly linked to higher levels of implementation.

Using structural equation modeling, the researchers found a significant positive relationship between managerial attitudes and AI adoption. This confirms that even in resource-constrained environments, psychological and perceptual factors play a central role in technology uptake.

In small, owner-managed restaurants, decision-making authority is highly centralized. This amplifies the influence of individual beliefs and perceptions, making managerial attitudes a key determinant of whether AI tools are adopted. However, the study also highlights that AI adoption remains partial and uneven. Even when attitudes are favorable, implementation tends to be incremental rather than comprehensive. Restaurants often experiment with specific tools rather than fully integrating AI into their operations.

This reflects broader constraints identified in the research, including limited financial resources, lack of technical expertise, and concerns about operational disruption. Managers must balance potential efficiency gains with risks related to costs, workforce impact, and customer experience.

The findings align with established technology acceptance theories, which emphasize that perceived usefulness and ease of use influence adoption. However, the study extends this framework by showing that ethical considerations, trust, and value alignment are equally important in small hospitality businesses.

Notably, the absence of negative attitudes alone does not lead to adoption. Managers must actively perceive AI as relevant and beneficial to their specific operational context. This underscores the importance of demonstrating practical, tangible use cases rather than promoting AI as a general solution.

Financial performance shapes adoption indirectly

The study finds no direct link between AI adoption and financial performance. Instead, revenue acts as a contextual factor that influences how strongly attitudes translate into action.

Using additional analysis, the researchers show that in lower-revenue restaurants, managerial attitudes have a stronger impact on AI adoption than in higher-revenue firms. In these financially constrained environments, decisions are more tightly linked to the owner's personal evaluations, making attitudes a more decisive factor.

On the other hand, higher-revenue businesses may rely on a broader set of organizational considerations, including strategic planning, resource allocation, and operational capacity. This dilutes the direct influence of managerial attitudes.

Similarly, the negative relationship between environmental CSR and AI attitudes is more pronounced in lower-revenue firms. This suggests that financial constraints amplify the importance of value-based decision-making, as managers must carefully assess whether AI investments align with their priorities.

Overall, financial performance does not directly drive CSR practices or AI adoption. Instead, it conditions how these factors interact, shaping the strength and direction of relationships within the decision-making process. This challenges traditional assumptions that higher revenues enable greater adoption of both CSR and digital technologies. In small restaurant SMEs, CSR is primarily value-driven rather than resource-driven, and AI adoption depends more on perception than on financial capacity alone.

Implications for policy and industry

The study suggests that regulatory compliance and general CSR promotion are insufficient to drive AI uptake. While responsible practices are already embedded in many small restaurants, they do not automatically translate into openness to new technologies.

Instead, targeted interventions are needed to demonstrate how AI can support existing practices, particularly in areas such as waste reduction, energy management, and operational efficiency. Practical, sector-specific applications are more likely to resonate with managers than abstract promises of innovation.

Next up, the research highlights the importance of addressing perceptual barriers. Training programs, advisory services, and pilot projects can help build trust and familiarity with AI, especially among managers who are cautious about its implications.

Third, financial support mechanisms should focus on lower-revenue firms, where attitudes play a more critical role in adoption. Subsidies, incentives, and accessible financing can help bridge the gap between positive perceptions and actual implementation.

For restaurant owners, the study stresses that AI adoption is not a guaranteed path to immediate financial gains. Instead, it should be viewed as part of a gradual transformation shaped by operational needs, managerial beliefs, and resource constraints. The research also suggests that aligning AI with sustainability goals could improve acceptance among environmentally oriented businesses. Tools that directly support waste reduction, energy efficiency, or local sourcing may be more appealing than generic automation solutions.